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nts/README.md

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+# NTS-Net
+
+This is a PyTorch implementation of the ECCV2018 paper "Learning to Navigate for Fine-grained Classification" (Ze Yang, Tiange Luo, Dong Wang, Zhiqiang Hu, Jun Gao, Liwei Wang).
+
+## Requirements
+- python 3+
+- pytorch 0.4+
+- numpy
+- datetime
+
+## Datasets
+Download the [CUB-200-2011](http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz) datasets and put it in the root directory named **CUB_200_2011**, You can also try other fine-grained datasets.
+
+## Train the model
+If you want to train the NTS-Net, just run ``python train.py``. You may need to change the configurations in ``config.py``. The parameter ``PROPOSAL_NUM`` is ``M`` in the original paper and the parameter ``CAT_NUM`` is ``K`` in the original paper. During training, the log file and checkpoint file will be saved in ``save_dir`` directory. You can change the parameter ``resume`` to choose the checkpoint model to resume.
+
+## Test the model
+If you want to test the NTS-Net, just run ``python test.py``. You need to specify the ``test_model`` in ``config.py`` to choose the checkpoint model for testing.
+
+## Model
+We also provide the checkpoint model trained by ourselves, you can download it from [here](https://drive.google.com/file/d/1F-eKqPRjlya5GH2HwTlLKNSPEUaxCu9H/view?usp=sharing). If you test on our provided model, you will get a 87.6% test accuracy.
+
+## Reference
+If you are interested in our work and want to cite it, please acknowledge the following paper:
+
+```
+@inproceedings{Yang2018Learning,
+author = {Yang, Ze and Luo, Tiange and Wang, Dong and Hu, Zhiqiang and Gao, Jun and Wang, Liwei},
+title = {Learning to Navigate for Fine-grained Classification},
+booktitle = {ECCV},
+year = {2018}
+}
+```